Human Concentrative Nucleoside Transporter 3 Transfection with Ultrasound and Microbubbles in Nucleoside Transport Deficient HEK293 Cells Greatly Increases Gemcitabine Uptake
Bibliographic record
Abstract
Gemcitabine is a hydrophilic clinical anticancer drug that requires nucleoside transporters to cross plasma membranes and enter cells. Pancreatic adenocarcinomas with low levels of nucleoside transporters are generally resistant to gemcitabine and are currently a clinical problem. We tested whether transfection of human concentrative nucleoside transporter 3 (hCNT3) using ultrasound and lipid stabilized microbubbles could increase gemcitabine uptake and sensitivity in HEK293 cells made nucleoside transport deficient by pharmacologic treatment with dilazep. To our knowledge, no published data exists regarding the utility of using hCNT3 as a therapeutic gene to reverse gemcitabine resistance. Our ultrasound transfection system--capable of transfection of cell cultures, mouse muscle and xenograft CEM/araC tumors--increased hCNT3 mRNA and (3)H-gemcitabine uptake by >2,000- and 3,400-fold, respectively, in dilazep-treated HEK293 cells. Interestingly, HEK293 cells with both functional human equilibrative nucleoside transporters and hCNT3 displayed 5% of (3)H-gemcitabine uptake observed in cells with only functional hCNT3, suggesting that equilibrative nucleoside transporters caused significant efflux of (3)H-gemcitabine. Efflux assays confirmed that dilazep could inhibit the majority of (3)H-gemcitabine efflux from HEK293 cells, suggesting that hENTs were responsible for the majority of efflux from the tested cells. Oocyte uptake transport assays were also performed and provided support for our hypothesis. Gemcitabine uptake and efflux assays were also performed on pancreatic cancer AsPC-1 and MIA PaCa-2 cells with similar results to that of HEK293 cells. Using the MTS proliferation assay, dilazep-treated HEK293 cells demonstrated 13-fold greater resistance to gemcitabine compared to dilazep-untreated HEK293 cells and this resistance could be reversed by transfection of hCNT3 cDNA. We propose that transfection of hCNT3 cDNA using ultrasound and microbubbles may be a method to reverse gemcitabine resistance in pancreatic tumors that have little nucleoside transport activity which are resistant to almost all current anticancer therapies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".